Oftentimes, users desire to use the appearance of real-world objects as references for the surfaces of computer-generated objects; for example, using the appearance of a wood-grain desk in their workspace as a reference for the surface of a computer-generated desk. Creating a model of surface properties can allow the appearance of real-world objects to be applied in such a manner. Determining a bi-directional reflectance distribution function (BRDF) that defines how light is reflected at a surface can be used to create such a material model. Finding a BRDF of a surface allows for capture of the appearance of real-world surfaces for use as references when rendering computer-generated objects to produce high-quality photorealistic content. As such, the process of determining a material model that can accurately assign the appearance of material surface to a computer-generated object can be known as BRDF capture.
Currently, BRDF capture can be performed using imaging to determine information about the appearance of a surface of a real-world object. Typically, to perform BRDF capture, existing techniques rely on complex setups to collect photographs under a multitude of staged lighting conditions. The requirements of such a set-up make BRDF capture impracticable and inefficient as use of these complex systems is highly costly and requires significant time and effort. Approaches that attempt to overcome such complex system setups are limited to BRDF capture of highly uniform material surfaces where there are little to no variations in the surface properties across the material surface. As such, these approaches fail to allow for accurate BRDF capture of surfaces with material variations, for example, differences in wood grain texture, etchings in a metal surface, or variations in leather coloration across a captured surface.